Empirical model-building and response surface
Empirical model-building and response surface
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Self-organization of markets: an example of a computational approach
Computational Economics - Special issue: genetic algorithms
Bandit problems and the exploration/exploitation tradeoff
IEEE Transactions on Evolutionary Computation
Inductive reasoning and bounded rationality reconsidered
IEEE Transactions on Evolutionary Computation
A game-theoretic and dynamical-systems analysis of selection methods in coevolution
IEEE Transactions on Evolutionary Computation
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Standards of evidence in scientific work, by the very term "standards," should be consistent, but they are not. Often, well-known "facts" or claims turn out to be wrong, disagreements over the interpretation of data and methods yield to political motivations. Even people who would have us strive for the highest aspirations of scientific quality defend arguments from vox populi, or at least majority rule. This chapter will discuss the standards of evidence in scientific work, with particular emphasis on evolutionary computation and modeling complex adaptive systems. Evidence shows that some models of seemingly simple systems are really quite complicated. In other cases, adjusting assumptions about a model leads to results that are at significant variance from what is commonly accepted. The implications of accepting well-known models of these systems are explored. Two common concepts are identified as being associated with potential problematic models: expectation and equilibrium.